CAREER: Extrapolatable, Uncertainty-Quantified Modeling of Nitrogen Kinetics Informed by Data Across Multiple Scales

预测性计算机模型具有巨大的潜力，可以更快、更便宜地设计出更清洁、更高效的发动机，而这正是我们的社会和地球迫切需要的。为了对发动机设计产生最大的影响，模型必须在已知不确定性的情况下做出准确的预测，特别是对于从未制造或测试的新的尖端发动机设计。这样的预测模型对于最大限度地减少氮氧化物(NOx)的形成特别有用，氮氧化物是烟雾、地面臭氧和其他对人类和环境健康有害的影响的罪魁祸首。该项目的目标是创建和验证燃烧过程中NOx生成的预测模型。一种利用现代数据科学和计算化学的创新方法将用于创建具有已知不确定性的预测模型。由此产生的方法和模型将成为未来研究所有传统燃料和替代燃料燃烧过程中NOx形成的基础。该项目还将吸引当地高中生参与化学和数据科学项目，为高中和大学教师创建和传播教案，并与业界合作，使研究能够立即导致更好的发动机设计。技术目标是通过优化选择、创建和利用从分子到宏观尺度的数据，创建一个可外推、不确定性量化的基础NOx动力学模型。该方法融合了(1)理论计算以创建分子数据并开发速率定律和混合规则来表示反应速率的压力和组成关系，(2)实验测量以在最能为发动机预测提供信息的条件下收集宏观数据，以及(3)基于多尺度数据的不确定性量化建模以创建可用于预测设计的可信模型。这项工作主要集中在尚未发现的路径上，假设在高效率、低NOx发动机的高压和低峰值温度下，包括一条主要的NOx路线。总之，这项研究将解决目前对高压下NOx生成的关键悬而未决的问题，并产生第一个受多尺度数据约束的不确定性量化的NOx动力学模型。更广泛地说，目前的速率定律、混合规则和多尺度数据驱动方法也将使许多其他化学反应气体能够更好地建模、模拟代码和理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Predictive computer models have immense potential to enable faster, cheaper design of cleaner, more efficient engines that our society and planet urgently need. To have the highest impact on designing engines, models must make accurate predictions with known uncertainty, especially for new cutting-edge engine designs never manufactured or tested. Such predictive models would be especially useful for minimizing the formation of nitrogen oxides (NOx), which are responsible for smog, ground-level ozone, and other effects detrimental to human and environmental health. The goal of this project is to create and validate a predictive model for NOx formation during combustion. An innovative approach, which leverages modern data science and computational chemistry, will be used to create predictive models with known uncertainty. The resulting methodology and models will then form the backbone for future studies of NOx formation during combustion of all conventional and alternative fuels. The project will also engage local high-school students in chemistry and data-science projects, create and disseminate lesson plans for high-school and university teachers, and partner with industry to enable the research to lead to better engine designs immediately.The technical objective is to create an extrapolatable, uncertainty-quantified, foundational NOx kinetic model by optimally selecting, creating, and exploiting data from molecular to macroscopic scales. The approach fuses (1) theoretical calculations to create molecular data and develop rate laws and mixture rules to represent the pressure and composition dependence of reaction rates, (2) experimental measurements to gather macroscopic data at conditions that best inform engine predictions, and (3) uncertainty-quantified modeling based on multiscale data to create models trustable for predictive design. This work largely centers on undiscovered pathways hypothesized to comprise a major NOx route at the high pressures and low peak temperatures of high-efficiency, low-NOx engines. Altogether, the research will address key outstanding issues in the present understanding of NOx formation at high pressures and produce the first uncertainty-quantified NOx kinetic model constrained by multiscale data. More broadly, the present rate laws, mixture rules, and multiscale data-driven approach will also enable better models of, simulation codes for, and understanding of many other chemically reacting gases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.